(Charlie) Yichuan Tang Ph.D.

Deep Learning Researcher, Engineer, Entrepreneur

University of Toronto
last name [at] cs.toronto.edu

Curriculum Vitae
Google Scholar  


I work in the field of Deep Learning and statistical Machine Learning.
Currently, I'm a Research Scientist at Apple Inc.

In 2015, I obtained my Ph.D. from the University of Toronto (Machine Learning),
under the supervision of Geoffrey Hinton and Ruslan Salakhutdinov.

I have broad research interests, which include applications to robotics/autonomy,
computer vision, natural language processing, and reinforcement learning.

Recent Research Highlights:

  • Predicting future trajectories (NeurIPS '19): [Arxiv Link]
  • Learning roboust and risk-sensitive deep RL policies (CoRL '19): [Arxiv Link]
  • Can we use self-play to learning complex negotiations?
           (Autonomous Driving Wkshp, ICCVW '19): [Paper Link]

Selected Publications

See full list of publications

Multiple Futures Prediction

Yichuan Charlie Tang, Ruslan Salakhutdinov.  Neural Information Processing Systems.   (NeurIPS 2019)

Worst Cases Policy Gradients

Yichuan Charlie Tang, Jian Zhang, Ruslan Salakhutdinov.  Conference on Robot Learning.   (CoRL 2019)

Towards Learning Multi-agent Negotiations via Self-Play

Yichuan Charlie Tang.  Autonomous Driving Workshop, International Conference on Computer Vision.   (ICCVW 2019)

Relational Mimic for Visual Adversarial Imitation Learning

Lionel Blonde, Yichuan Charlie Tang, Jian Zhang, Russ Webb.   Arxiv Preprint. arxiv.org/abs/1912.08444   (Arxiv 2019)

Learning Generative Models using Visual Attention

Yichuan Charlie Tang, Nitish Srivastava, Ruslan Salakhutdinov.  Neural Information Processing Systems.   (NIPS 2014, Oral)

Learning Stochastic Feedforward Neural Networks

Yichuan Charlie Tang, Ruslan Salakhutdinov.  Neural Information Processing Systems.   (NIPS 2013)

Tensor Analyzers

Yichuan Charlie Tang, Ruslan Salakhutdinov, Geoffrey Hinton.   International Conference on Machine Learning   (ICML 2013)

Deep Learning using Linear Support Vector Machines

Yichuan Charlie Tang.  Arxiv Preprint. https://arxiv.org/abs/1306.0239   (Arxiv 2013)

Deep Mixtures of Factor Analyzers

Yichuan Charlie Tang, Ruslan Salakhutdinov, Geoffrey Hinton.   International Conference on Machine Learning   (ICML 2012, Oral)

Deep Lambertian Networks

Yichuan Charlie Tang, Ruslan Salakhutdinov, Geoffrey Hinton.   International Conference on Machine Learning   (ICML 2012, Oral)

Robust Boltzmann Machines for Denoising and Recognition

Yichuan Charlie Tang, Ruslan Salakhutdinov, Geoffrey Hinton.   IEEE Conference on Computer Vision and Pattern Recognition   (CVPR 2012)

Multiresolution Deep Belief Networks

Yichuan Charlie Tang, Abdul-rahman Mohamed.  15th International Conference on Artificial Intelligence and Statistics   (AISTATS 2012)

A Large Model of the Functioning Brain

Chris Eliasmith, Terry Stewart, Xuan Choo, Trevor Bekolay, Travis DeWolf, Yichuan Charlie Tang, Daniel Rasmussen.  Science 30, November 2012.   (Science)

Deep Networks for Robust Visual Recognition

Yichuan Charlie Tang, Chris Eliasmith.  International Conference on Machine Learning   (ICML 2010)
 

Code

 

Bio

Charlie's research interests include Deep Learning, Vision, Reinforcement Learning, Neuroscience, and Robotics. He is one of the few competitors to have reached the #1 ranking on Kaggle.com, a widely popular machine learning competition platform. Charlie obtained his PhD in 2015 in Machine Learning from the University of Toronto. His thesis focused on various aspects of Deep Learning technology. Charlie also holds a Bachelors in Mechatronics Engineering and Masters in Computer Science from the University of Waterloo. Charlie is also a Canadian national chess master, and a two time (2001, 2002) high school chess champion of the state of Ohio.